Multi-Omics Marker Analysis Enables Early Prediction of Breast Tumor Progression

Show simple item record Xu, Haifeng Lien, Tonje Bergholtz, Helga Fleischer, Thomas Djerroudi, Lounes Vincent-Salomon, Anne Sorlie, Therese Aittokallio, Tero 2021-07-20T09:33:01Z 2021-07-20T09:33:01Z 2021-06-03
dc.identifier.citation Xu , H , Lien , T , Bergholtz , H , Fleischer , T , Djerroudi , L , Vincent-Salomon , A , Sorlie , T & Aittokallio , T 2021 , ' Multi-Omics Marker Analysis Enables Early Prediction of Breast Tumor Progression ' , Frontiers in Genetics , vol. 12 , 670749 .
dc.identifier.other PURE: 166762722
dc.identifier.other PURE UUID: 53dc695e-027b-483a-ab12-d9c666cc5db9
dc.identifier.other WOS: 000662189000001
dc.identifier.other ORCID: /0000-0002-0886-9769/work/97268886
dc.description.abstract Ductal carcinoma in situ (DCIS) is a preinvasive form of breast cancer with a highly variable potential of becoming invasive and affecting mortality of the patients. Due to the lack of accurate markers of disease progression, many women with detected DCIS are currently overtreated. To distinguish those DCIS cases who are likely to require therapy from those who should be left untreated, there is a need for robust and predictive biomarkers extracted from molecular or genetic profiles. We developed a supervised machine learning approach that implements multi-omics feature selection and model regularization for the identification of biomarker combinations that could be used to distinguish low-risk DCIS lesions from those with a higher likelihood of progression. To investigate the genetic heterogeneity of disease progression, we applied this approach to 40 pure DCIS and 259 invasive breast cancer (IBC) samples profiled with genome-wide transcriptomics, DNA methylation, and DNA copy number variation. Feature selection using the multi-omics Lasso-regularized algorithm identified both known genes involved in breast cancer development, as well as novel markers for early detection. Even though the gene expression-based model features led to the highest classification accuracy alone, methylation data provided a complementary source of features and improved especially the sensitivity of correctly classifying DCIS cases. We also identified a number of repeatedly misclassified DCIS cases when using either the expression or methylation markers. A small panel of 10 gene markers was able to distinguish DCIS and IBC cases with high accuracy in nested cross-validation (AU-ROC = 0.99). The marker panel was not specific to any of the established breast cancer subtypes, suggesting that the 10-gene signature may provide a subtype-agnostic and cost-effective approach for breast cancer detection and patient stratification. We further confirmed high accuracy of the 10-gene signature in an external validation cohort (AU-ROC = 0.95), profiled using distinct transcriptomic assay, hence demonstrating robustness of the risk signature. en
dc.format.extent 14
dc.language.iso eng
dc.relation.ispartof Frontiers in Genetics
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject risk signature
dc.subject breast cancer
dc.subject disease progression
dc.subject early detection
dc.subject machine learning
dc.subject FOLLOW-UP
dc.subject CANCER
dc.subject EXPRESSION
dc.subject GRADE
dc.subject BIOPSY
dc.subject MODELS
dc.subject CELLS
dc.subject 1184 Genetics, developmental biology, physiology
dc.title Multi-Omics Marker Analysis Enables Early Prediction of Breast Tumor Progression en
dc.type Article
dc.contributor.organization Institute for Molecular Medicine Finland
dc.contributor.organization Helsinki Institute for Information Technology
dc.contributor.organization Bioinformatics
dc.description.reviewstatus Peer reviewed
dc.relation.issn 1664-8021
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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